Technology Reports of Kansai University

Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.

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Submission Deadline

Volume - 63 , Issue 09
10 Oct 2021
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Upcoming Publication

Volume - 63 , Issue 05
31 Aug 2021

Aim and Scope

Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:

Electrical Engineering and Telecommunication Section:

Electrical Engineering, Telecommunication Engineering, Electro-mechanical System Engineering, Biological Biosystem Engineering, Integrated Engineering, Electronic Engineering, Hardware-software co-design and interfacing, Semiconductor chip, Peripheral equipments, Nanotechnology, Advanced control theories and applications, Machine design and optimization , Turbines micro-turbines, FACTS devices , Insulation systems , Power quality , High voltage engineering, Electrical actuators , Energy optimization , Electric drives , Electrical machines, HVDC transmission, Power electronics.

Computer Science Section :

Software Engineering, Data Security , Computer Vision , Image Processing, Cryptography, Computer Networking, Database system and Management, Data mining, Big Data, Robotics , Parallel and distributed processing , Artificial Intelligence , Natural language processing , Neural Networking, Distributed Systems , Fuzzy logic, Advance programming, Machine learning, Internet & the Web, Information Technology , Computer architecture, Virtual vision and virtual simulations, Operating systems, Cryptosystems and data compression, Security and privacy, Algorithms, Sensors and ad-hoc networks, Graph theory, Pattern/image recognition, Neural networks.

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

Chemical engineering fundamentals, Physical, Theoretical and Computational Chemistry, Chemical engineering educational challenges and development, Chemical reaction engineering, Chemical engineering equipment design and process design, Thermodynamics, Catalysis & reaction engineering, Particulate systems, Rheology, Multifase flows, Interfacial & colloidal phenomena, Transport phenomena in porous/granular media, Membranes and membrane science, Crystallization, distillation, absorption and extraction, Ionic liquids/electrolyte solutions.

Food Engineering :

Food science, Food engineering, Food microbiology, Food packaging, Food preservation, Food technology, Aseptic processing, Food fortification, Food rheology, Dietary supplement, Food safety, Food chemistry. AMA, Agricultural Mechanization in Asia, Africa and Latin America Teikyo Medical Journal Azerbaijan Medical Journal

Physics Section:

Astrophysics, Atomic and molecular physics, Biophysics, Chemical physics, Civil engineering, Cluster physics, Computational physics, Condensed matter, Cosmology, Device physics, Fluid dynamics, Geophysics, High energy particle physics, Laser, Mechanical engineering, Medical physics, Nanotechnology, Nonlinear science, Nuclear physics, Optics, Photonics, Plasma and fluid physics, Quantum physics, Robotics, Soft matter and polymers.

Mathematics Section:

Actuarial science, Algebra, Algebraic geometry, Analysis and advanced calculus, Approximation theory, Boundry layer theory, Calculus of variations, Combinatorics, Complex analysis, Continuum mechanics, Cryptography, Demography, Differential equations, Differential geometry, Dynamical systems, Econometrics, Fluid mechanics, Functional analysis, Game theory, General topology, Geometry, Graph theory, Group theory, Industrial mathematics, Information theory, Integral transforms and integral equations, Lie algebras, Logic, Magnetohydrodynamics, Mathematical analysis.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-10-08-2021-11475
Total View : 367

Title : BIG DATA TESTING - CHALLENGES AND BEST PRACTICES

Abstract :

In recent times, the term ‘Big Data’ has been under the limelight due to its exponential increase in relevance and importance in small, medium, large and very large companies. Industries of divergent sectors such as education, health, agriculture and telecommunication are all leveraging on the power of Big Data to enhance business prospects along with improved customer experience. Testing such highly volatile data, which is unstructured data and generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, which are quite challenging in order to derive maximum benefit in information processing and decision making from the use of big data, such data must be of acceptable quality and must be fairly usable in terms of interloper- ability, relevance, and accuracy. However, such data quality can only be guaranteed if systems from which these data are harvested are adequately tested to ensure that output data from such systems exhibits minimum big data quality standards and characteristics. The methods adopted include Test criterion and cases which consider the volatility of big data and its underlying characteristics which is not limited to, but include Volume, Velocity, Veracity and Variety. Testing such highly volatile data, which is unstructured. Hadoop and Map Reduce which are also tools for testing big data. One of the most challenging endeavour for a tester is how to keep pace with changing dynamics of the industry. This work discusses inherent challenges faced in big data testing and the respective best practices that can be adopted in big data testing to enhance big data quality and accuracy.

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Journal ID : TRKU-08-08-2021-11474
Total View : 578

Title : Simulation of Diffuser Parameters in the Performance of Horizontal Axis Wind Turbine using Computational Fluid Dynamics

Abstract :

Wind energy is one of the promising alternative energy resources after solar and hydropower. Most of wind turbine technologies are designed at high speed, whereas, not effectively operated in low wind speed areas. An effective technology is required to enhance the possible use of wind energy at low wind speeds. Diffuser Augmented Wind Turbine (DAWT) has been used recently to improve the use of wind turbine in a low wind speed area by manipulating the wind speed. The main concept of this technology is the pressure difference between inside and outside of DAWT which is occurred, hence, it might enhance the wind velocity and the power is increased as well. In this paper, simulation using ANSYS was conducted to investigate the performance of Horizontal Axis Wind Turbine (HAWT) in low wind speed area applying DAWT by modifying the angle and the length of diffuser. The variation of the diffuser angle was in the range 4-16o at L=1.25D. The simulation results showed a good agreement with the reference literature which obtained the increased power around 1.4-2.9 times higher than the non-diffuser wind turbine. The parameter of diffuser length was also investigated at L=0.25D-2.5D, with the significant impacts are obtained until L=1.25D

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